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A Framework for testing Federated Learning algorithms using an edge-like environment
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DOI：10.1016/j.future.2024.107626.png)
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Abstract
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Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a singlecentralized model while keeping their data private and decentralized. FL is commonly used in edge computing,which involves placing computer workloads (both hardware and software) as close as possible to the edge,where data are created and where actions are occurring, enabling faster response times, greater data privacy,and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, itis non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation.This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. Ingeneral, testing and evaluating FL algorithms can be a very difficult and complex task due to the distributednature of the systems. In this work, a framework is proposed and implemented to evaluate FL algorithms in amore easy and scalable way. This framework is evaluated over a distributed edge-like environment managedby a container orchestration platform (i.e. Kubernetes).
Keywords:
Federated learning
Edge computing
Kubernetes
Microservices
Development framework
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